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            Struct Input
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       <span id="exhale-struct-structtorch-tensorrt-1-1input">
       </span>
       <h1 id="cpp-api-structtorch-tensorrt-1-1input--page-root">
        Struct Input
        <a class="headerlink" href="#cpp-api-structtorch-tensorrt-1-1input--page-root" title="Permalink to this headline">
         ¶
        </a>
       </h1>
       <ul class="simple">
        <li>
         <p>
          Defined in
          <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h">
           <span class="std std-ref">
            File torch_tensorrt.h
           </span>
          </a>
         </p>
        </li>
       </ul>
       <h2 id="struct-documentation">
        Struct Documentation
        <a class="headerlink" href="#struct-documentation" title="Permalink to this headline">
         ¶
        </a>
       </h2>
       <dl class="cpp struct">
        <dt id="_CPPv4N14torch_tensorrt5InputE">
         <span class="target" id="structtorch__tensorrt_1_1Input">
         </span>
         <em class="property">
          struct
         </em>
         <code class="sig-prename descclassname">
          torch_tensorrt
          <code class="sig-prename descclassname">
           ::
          </code>
         </code>
         <code class="sig-name descname">
          Input
         </code>
         <a class="headerlink" href="#_CPPv4N14torch_tensorrt5InputE" title="Permalink to this definition">
          ¶
         </a>
         <br/>
        </dt>
        <dd>
         <p>
          A struct to hold an input range (used by TensorRT Optimization profile)
         </p>
         <p>
          This struct can either hold a single vector representing an input shape, signifying a static input shape or a set of three input shapes representing the min, optiminal and max input shapes allowed for the engine.
         </p>
         <div class="breathe-sectiondef docutils container">
          <p class="breathe-sectiondef-title rubric">
           Public Functions
          </p>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a2c8216e7e14f7fdad145d53820eafd89">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            std::vector&lt;int64_t&gt;
            <em>
             shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   shape
                  </span>
                 </code>
                 :
                 <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
                  <span class="std std-ref">
                   Input
                  </span>
                 </a>
                 tensor shape
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a95c05ba3ba477e3915953c1a4c915be6">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            std::vector&lt;int64_t&gt;
            <em>
             shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType">
             DataType
            </a>
            <em>
             dtype
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format.
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   shape
                  </span>
                 </code>
                 :
                 <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
                  <span class="std std-ref">
                   Input
                  </span>
                 </a>
                 tensor shape
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   dtype
                  </span>
                 </code>
                 : Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32)
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a983a2859661e1ce26275918f61632695">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            c10::ArrayRef&lt;int64_t&gt;
            <em>
             shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   shape
                  </span>
                 </code>
                 :
                 <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
                  <span class="std std-ref">
                   Input
                  </span>
                 </a>
                 tensor shape
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a6aa880180a17dc428745f5818e591e24">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            c10::ArrayRef&lt;int64_t&gt;
            <em>
             shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType">
             DataType
            </a>
            <em>
             dtype
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format.
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   shape
                  </span>
                 </code>
                 :
                 <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
                  <span class="std std-ref">
                   Input
                  </span>
                 </a>
                 tensor shape
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   dtype
                  </span>
                 </code>
                 : Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32)
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a9b45781bf07a911ed809b2dcf806ec65">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            std::vector&lt;int64_t&gt;
            <em>
             min_shape
            </em>
            , std::vector&lt;int64_t&gt;
            <em>
             opt_shape
            </em>
            , std::vector&lt;int64_t&gt;
            <em>
             max_shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   min_shape
                  </span>
                 </code>
                 : Minimum shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   opt_shape
                  </span>
                 </code>
                 : Target optimization shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   max_shape
                  </span>
                 </code>
                 : Maximum acceptible shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a898d8d54ffd759748398ae2cd98254d8">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            std::vector&lt;int64_t&gt;
            <em>
             min_shape
            </em>
            , std::vector&lt;int64_t&gt;
            <em>
             opt_shape
            </em>
            , std::vector&lt;int64_t&gt;
            <em>
             max_shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType">
             DataType
            </a>
            <em>
             dtype
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object for a dynamic input size from vectors for minimum shape, optimal shape, and max shape supported sizes optional arguments allow the user to configure expected input shape tensor format.
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   min_shape
                  </span>
                 </code>
                 : Minimum shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   opt_shape
                  </span>
                 </code>
                 : Target optimization shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   max_shape
                  </span>
                 </code>
                 : Maximum acceptible shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   dtype
                  </span>
                 </code>
                 : Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32)
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a6c99d70eb1da005fea790f83a0c005cf">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            c10::ArrayRef&lt;int64_t&gt;
            <em>
             min_shape
            </em>
            , c10::ArrayRef&lt;int64_t&gt;
            <em>
             opt_shape
            </em>
            , c10::ArrayRef&lt;int64_t&gt;
            <em>
             max_shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   min_shape
                  </span>
                 </code>
                 : Minimum shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   opt_shape
                  </span>
                 </code>
                 : Target optimization shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   max_shape
                  </span>
                 </code>
                 : Maximum acceptible shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a60447f3c15f82a1ab4f9621a608b7b0a">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            c10::ArrayRef&lt;int64_t&gt;
            <em>
             min_shape
            </em>
            , c10::ArrayRef&lt;int64_t&gt;
            <em>
             opt_shape
            </em>
            , c10::ArrayRef&lt;int64_t&gt;
            <em>
             max_shape
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType">
             DataType
            </a>
            <em>
             dtype
            </em>
            ,
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <em>
             format
            </em>
            =
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            ::kContiguous
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes.
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   min_shape
                  </span>
                 </code>
                 : Minimum shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   opt_shape
                  </span>
                 </code>
                 : Target optimization shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   max_shape
                  </span>
                 </code>
                 : Maximum acceptible shape for input tensor
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   dtype
                  </span>
                 </code>
                 : Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32)
                </p>
               </li>
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   format
                  </span>
                 </code>
                 : Expected tensor format for the input (Defaults to contiguous)
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
          <dl class="cpp function">
           <dt id="_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1ab22f3643c48f8fea5b4f92566acbfe3d">
            </span>
            <code class="sig-name descname">
             Input
            </code>
            <span class="sig-paren">
             (
            </span>
            at::Tensor
            <em>
             tensor
            </em>
            <span class="sig-paren">
             )
            </span>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Construct a new
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             spec object using a torch tensor as an example The tensor’s shape, type and layout inform the spec’s values.
            </p>
            <p>
             Note: You cannot set dynamic shape through this method, you must use an alternative constructor
            </p>
            <p>
            </p>
            <dl class="simple">
             <dt>
              <strong>
               Parameters
              </strong>
             </dt>
             <dd>
              <ul class="breatheparameterlist simple">
               <li>
                <p>
                 <code class="docutils literal notranslate">
                  <span class="pre">
                   tensor
                  </span>
                 </code>
                 : Reference tensor to set shape, type and layout
                </p>
               </li>
              </ul>
             </dd>
            </dl>
           </dd>
          </dl>
         </div>
         <div class="breathe-sectiondef docutils container">
          <p class="breathe-sectiondef-title rubric">
           Public Members
          </p>
          <dl class="cpp var">
           <dt id="_CPPv4N14torch_tensorrt5Input9min_shapeE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1af95ee167b1e25e1a599a8fc67ffea9ac">
            </span>
            std::vector&lt;int64_t&gt;
            <code class="sig-name descname">
             min_shape
            </code>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9min_shapeE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Minimum acceptable input size into the engine.
            </p>
           </dd>
          </dl>
          <dl class="cpp var">
           <dt id="_CPPv4N14torch_tensorrt5Input9opt_shapeE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a63666d30decf34115f642e75f0cf8256">
            </span>
            std::vector&lt;int64_t&gt;
            <code class="sig-name descname">
             opt_shape
            </code>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9opt_shapeE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Optimal input size into the engine (size optimized for given kernels accept any size in min max range)
            </p>
           </dd>
          </dl>
          <dl class="cpp var">
           <dt id="_CPPv4N14torch_tensorrt5Input9max_shapeE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1ad3dfdcaaa9126dcb8dfa08dec085d589">
            </span>
            std::vector&lt;int64_t&gt;
            <code class="sig-name descname">
             max_shape
            </code>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9max_shapeE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Maximum acceptable input size into the engine.
            </p>
           </dd>
          </dl>
          <dl class="cpp var">
           <dt id="_CPPv4N14torch_tensorrt5Input5shapeE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a26997c30b04c8013a5afadb181b70377">
            </span>
            std::vector&lt;int64_t&gt;
            <code class="sig-name descname">
             shape
            </code>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5shapeE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             <a class="reference internal" href="#structtorch__tensorrt_1_1Input">
              <span class="std std-ref">
               Input
              </span>
             </a>
             shape to be fed to TensorRT, in the event of a dynamic shape, -1’s will hold the place of variable dimensions
            </p>
           </dd>
          </dl>
          <dl class="cpp var">
           <dt id="_CPPv4N14torch_tensorrt5Input5dtypeE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1a05d520340d486851d9e8a4797611895e">
            </span>
            <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType">
             DataType
            </a>
            <code class="sig-name descname">
             dtype
            </code>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5dtypeE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Expected data type for the input.
            </p>
           </dd>
          </dl>
          <dl class="cpp var">
           <dt id="_CPPv4N14torch_tensorrt5Input6formatE">
            <span class="target" id="structtorch__tensorrt_1_1Input_1ad544c1a1584d2f45bccf0e4963b8d292">
            </span>
            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat">
             TensorFormat
            </a>
            <code class="sig-name descname">
             format
            </code>
            <a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input6formatE" title="Permalink to this definition">
             ¶
            </a>
            <br/>
           </dt>
           <dd>
            <p>
             Expected tensor format for the input.
            </p>
           </dd>
          </dl>
         </div>
        </dd>
       </dl>
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